- International Journal of Information and Computation Technology
- Journal of Computational Intelligence in Bioinformatics
- Journal of Biological Control
- Digital Image Processing
- Biometrics and Bioinformatics
- Artificial Intelligent Systems and Machine Learning
- ICTACT Journal on Image and Video Processing
- ICTACT Journal on Soft Computing
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Meena, K.
- Literature Survey on the Prediction of Secondary Structure of Proteins Using Radial Basis Function Neural Networks (RBFNN) and Support Vector Machines (SVM)
Authors
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
International Journal of Information and Computation Technology, Vol 3, No 1 (2013), Pagination: 33-43Abstract
The Protein structure prediction has been an active research area for the last 40 years or so. The technical progress in computational Molecular Biology during the last decades has contributed significantly to the progress we see today. The major goal of predicting Protein structures underpins the correct assumption that three dimensional structures confer protein function. The linear Amino Acid sequences must transform to nonlinear Secondary Structures and then to Tertiary and Quaternary Structures that are responsible for biological functions. Biological functions may remain similar or change in the related organisms through the evolutionary process. By considering the importance of the prediction of secondary structure of protein a detailed literature study of the same using Radial Basis Function Neural Networks (RBFNN) AND Support Vector Machines (SVM) has been reviewed in this paper.Keywords
Radial Basis Function Neural Networks, Support Vector Machines, Secondary Structures, Tertiary Structure And Quaternary StructuresReferences
- David T. Jones, “Protein Secondary Structure Prediction Based on Positionspecific Scoring Matrices, ” University of Warwick, Coventry CV4 7AL, United Kingdom, 1999.
- Wootton, J.C. and Federhen, S., “Statistics of local complexity in amino acid sequences and sequence databases”, Computers and Chemistry 17, 149-163 1993.
- Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer1, Jinghui Zhang, “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs”, Received June 20, 1997; Revised and Accepted July 16
- L., Leopold, J.L., Frank, R.L. and Maglia, A.M., "Protein Secondary Structure Prediction Using Rule Induction from Coverings", Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (part of IEEE Symposium Series on Computational Intelligence 2009), Nashville, Tennessee, USA, pp. 79-86, 2009.
- Rost, B., “Rising accuracy of protein secondary structure prediction”, D.Chasman, Ed., “Protein structure determination, analysis, and modeling for drug discovery”, New York: Dekker, pp. 207-249, 2003.
- Kabsh, W. and Sander, C., “How good are predictions of protein secondary structure, ” FEBS Letters, 155, pp. 179-182, 1983.
- Kloczkowski, A., Taner Z. Sen, “Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence”, Proteins, 49, 154-166, 2002.
- Garnier, J.J., Gibrat, J.F. and Robson, “GOR method for predicting protein secondary structure from amino acid sequence, Methods Enzymol”, 266, 540-550, 1996.
- Sung-Joon Park, “A Study of Fragment-Based Protein Structure Prediction: Biased Fragment Replacement for Searching Low-Energy Conformation”, 104-115, 2005.
- Karplus, K., Karchin, R., Draper, J., Casper, J., Mandel-Gutfreund, Y., Diekhans, M. and Hughey, R., “Combining local-structure, fold-recognition, and new fold methods for protein structure prediction”, Proteins, 53:491-496, 2003
- Kolodny, R., Koehl, R., Guibas, L. and Levitt, M., “Small libraries of protein fragments model native protein structures accurately”, J. Mol. Biol., 323:297-307, 2002.
- Pearson, W.R. and Lipman, D.J. Improved tools for biological sequence comparison proc. Natl. Acad. Sci. U.S.A., 85, 2444-2448, 1998.
- Söding, J, Biegert, A. and Lupas, A.N., "The HHpred interactive server for protein homology detection and structure prediction, ” Nucleic Acids Research 33 ((Web Server issue)): W244-248, 2005.
- Battey, J.N., Kopp, J., Bordoli, L., Read, R.J., Clarke, N.D. and Schwede, T., "Automated server predictions in CASP7, ” Proteins 69 (S): 68-82, 2007.
- Wang Z-X. “Assessing the accuracy of secondary structure”. NatStruct Biol., 3:145-146. 1994.
- Luciano Brocchieri and Samuel Karlin, “How are close residues of protein structures distributed in primary sequence”, Proc. Natl. Acad. Sci. USA Vol. 92, 12136-12140, December 1995.
- Henrick, K. and Thornton, J.M., “PQS: a protein quaternary structure file server”, Trends. Biochem. Sci. 23:358-361, 1998.
- Fischer, D., Rychlewski, L., Elofsson, A., Pazos, F., Valencia, A., Rost, B., Ortiz, A.R. and Dunbrack, R.L.J., “Proteins”, Suppl. 5, 171-183, 2001.
- Richard O. Day, Gary B. Lamont and Ruth Pachter, “Protein Structure Prediction by Applying an Evolutionary Algorithm”, in December 2001.
- Zhen Zhang and Nan Jing, “Radial basis function method for prediction of protein secondary structure, ” International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1379-1383, 2008.
- Senapati, M.R., Vijaya, I. and Dash, P.K., “Rule Extraction from Radial Basis Functional Neural Networks by Using Particle Swarm Optimization”, Journal of Computer Science, 3(8): 592-599, 2007.
- Karayiannis, N.B. and Mi, G.W., “Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques, ” IEEE Trans. Neural Networks, Vol. 8, 1492-1506, 1997.
- Susan C. White, “NN3 Time Series Forecasting with Radial Basis Function Networks”.
- Bishop, C., “Improving the Generalization Properties of Radial Basis Function Neural Networks, ” Neural Computation, 3, 579 - 588, 1991.
- Kenneth J. McGarry, John Tait, Stefan Wermter and John Macintyre, “Rule- Extraction from Radial Basis Function Networks”, IEE, 613-618, 1999.
- Park, J. and Sandberg, I.W., “Approximation and radial-basis-function networks, ” Neural Comput., Vol. 5, 305-316, 1993.
- Cai, Y.D., Liu, X.J., Xu, X.B. and Zhou, G.P., “Support vector machines for predicting protein structural class”, BMC Bioinformatics, 15, 2001.
- Minh Ngoc Nguyen and Jagath C. Rajapakse, “Two-stage support vector machines for protein secondary structure prediction, ” Neural, Parallel & Scientific Computations, vol. 11, No. 2, 1-18, 2003.
- Hu, H.-J. P.C. Tai, J. He, R. Harrison, and Y. Pan, “Protein secondary structure prediction using support vector machine with a PSSM profile and an advanced tertiary classifier, ” IEEE Computational Systems Bioinformatics Conference. 213-214, 2005.
- Lukasz A. Kurgan, Mandana Rahbari and Leila Homaeian, “Impact of thePredicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins”, Proceedings of the 5th International Conference on Machine Learning and Applications, IEEE.180-186, 2006.
- Kristin K. Koretke, Zaida Luthey-Schulten and Peter G. Wolynes, “Selfconsistently optimized energy functions for protein structure prediction by molecul
- Jian-xiong Dong, Adam Krzyzak and Ching Y. Suen, "A Fast SVM Training Algorithm", S.-W. Lee and A. Verri (Eds.): SVM 2002, LNCS 2388. 53-67, 2002.
- Chao Chen, Lixuan Chen, Xiaoyong Zou and Peixiang Cai, “Prediction of Protein Secondary Structure Content by Using the Concept of Chou’s Pseudo Amino Acid Composition and Support Vector Machine”, Protein & Peptide Letters, 16, 27-31, 2009.D.
- DeCoste and Scholkopf, B., “Training invariant support vector machines”, Machine Learning, 46(1-3):161-190, 2002.
- Comparision of Prediction of Structure of Protein of Soy Beans Using Radial Basis Function Neural Networks with other Methods for Rs126 and PDB Data Sets
Authors
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
Journal of Computational Intelligence in Bioinformatics, Vol 6, No 1 (2013), Pagination: 49-57Abstract
In this paper Prediction of structure of Protein of Soy Beans using Radial Basis Function Neural Networks for RS126 Data set and PDB Data set has been made and compared with other traditional methods namely Chou-Fasman, GOR, APSSP, PHD, Prospect and SSpro.The training and testing sets for both have been taken into consideration to train and test the networks respectively. The major parameter for finding the accuracy of the protein secondary structure prediction is the per-residue prediction accuracy, Q3, which gives the percentage of all correctly predicted residues within the three-state (H, E, C) classes, and has also been employed for assessment of prediction approaches. The performance of the RBFNN protein secondary structure prediction models is evaluated based on their prediction accuracy . The accuracy of the developed approach is compared with other traditional methods to explore the performance of the proposed approach. It is found that the proposed techniques provide a prediction accuracy of about 81% which is very significant. The accuracy for different width of sliding windows. It clearly shows that, with the increase in the sliding window width the accuracy also increases.Keywords
RBFNN, Prediction Accuracy, Training Set, Test Set, Sliding WindowReferences
- Boscott, P.E., Barton, G.J. and Richards, W.G., “Secondary Structure Prediction for Modelling by Homology”, PEDS, Vol. 6, Issue 3, pp.261–266, January 1993.
- Marti-Renom et al., “Comparative protein structure modeling of genes and genomes,” Rev. Biophys. Biomol, Struct, 29:291-325, 2000.
- Solovyev, V.V. and Salamov, A.A., “Predicting alpha-helix and betastrand segments of globular proteins”. Computer Applications in the Biosciences, 10, 661-669, 1994.
- Pollastri, G., Przybylski, D., Rost, B. and Baldi, P., “Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins”, 47(2), 228–235, 2002.
- Karlin, S., Bucher, P., “Correlation analysis of amino acid usage in protein classes”, Proc Natl Acad Sci, USA 1992.
- Chou, P.Y., Fasman, G.D., “Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins”. Biochemistry 13(2):211-22, 1974.
- Garnier, J.J., Gibrat, J.F. and Robson, “GOR method for predicting protein secondary structure from amino acid sequence, Methods Enzymol”, 266, 540– 550, 1996.
- Marti-Renom et al., “Comparative protein structure modeling of genes and genomes,” Rev. Biophys. Biomol, Struct, 29:291-325, 2000
- Rost, B., “Rising accuracy of protein secondary structure prediction”, D.Chasman, Ed., “Protein structure determination, analysis, and modeling for drug discovery”, New York: Dekker, pp. 207–249, 2003.
- Seasonal Population Fluctuations of Cotton Bollworm, Helicoverpa armigera (Hubner) in Relation to Biotic and Abiotic Environmental Factors at Raichur, Karnataka, India
Authors
1 National Bureau of Agriculturally Important Insects, Post Bag No. 2491, H. A. Farm Post, Hebbal, Bellary Road, Bangalore 560024, Karnataka, IN
2 Shrimathi Indira Gandhi College, Tiruchirappalli 620002, Tamil Nadu, IN
3 Department of M.C.A., Shrimathi Indira Gandhi College, Tiruchirappalli 620002, Tamil Nadu, IN
4 Indian Institute of Horticultural Research, Hessaraghatta Lake Post, Bangalore 560089, Karnataka, IN
5 Department of Entomology, Agricultural Research Station, Raichur 584101, Karnataka, IN
Source
Journal of Biological Control, Vol 24, No 1 (2010), Pagination: 47-50Abstract
An attempt was made to study the effect of abiotic and naturally occurring biotic factors on Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae) with cotton as a model crop system. The results revealed that Chrysoperla sp. (carnea-group) (r = 0.344) was positively correlated with pest incidence and the weather parameters like maximum temperature (r = -0.309) and rainfall (r = -0.288) were negatively correlated with pest incidence. It was observed that post-monsoon season was most favourable for pest occurrence and it was more when the crop was in flowering and boll formation stage. Spiders and Chrysoperla sp. (carnea-group) were positively correlated with pest incidence during winter.Keywords
Helicoverpa armigera, Cotton, Season, Spiders, Chrysopids.- Decision Tree Induction Model for the Population Dynamics of Mirid Bug, Creontiodes biseratense (Distant) (Hemiptera: Miridae) and Its Natural Enemies
Authors
1 Department of M.C.A., Shrimathi Indira Gandhi College, Trichirappalli 620 002, Tamil Nadu, IN
2 Bharathidasan University, Trichirappalli 620 024, Tamil Nadu, IN
3 Department of Entomology, University of Agricultural Sciences, Raichur 584 102, IN
Source
Journal of Biological Control, Vol 27, No 2 (2013), Pagination: 88-94Abstract
The mirid bug, Creontiodes biseratense (Distant) (Hemiptera: Miridae) is as a serious pest of cotton crop. Forecasting model by linking the pest incidence with season, crop phenology, biotic and abiotic factors enable to understand the dynamics of pest occurrence likely to occur. A data mining technique decision tree induction model is proposed for forecasting the pest incidence and study the population dynamics of mirid bug, C. biseratense in relation to its natural enemies viz., spider Lycosa sp. and coccinellid Cheilomenes sexmaculata Fabricius and abiotic factors. The results of the decision tree agreed well with statistical analysis.Keywords
Creontiodes biseratense, Cotton, Spiders, Coccinellids, Decision Tree, Information Theory, Abiotic.References
- Anonymous, 2008a. Project coordinator’s report (2007– 08). All India co-ordinated cotton improvement project, pp. 4.
- Basak J, Krishnapuram R. 2005. Interpretable hierarchical clustering by constructing an un-supervised decision tree. IEEE Trans Knowl Data Engg 17 (1): 121–132.
- Ghavami S. 2008. The potential of predatory spiders as biological control agents of cotton pests in Tehran Provinces of Iran. Asian J Expl Sci. 22 (3): 303–306.
- Han J, Kamber M. 2001. Classification and prediction. pp. 285–375. In Data Mining Concepts and Techniques, 2nd ed. Jim Gray, Indian Reprint. Elsevier. Morgan Kaufmann.
- Khan M, Quade A, Murray D. 2007. Damage assessment and action threshold for mirids, Creontiades spp. In Bollgard II cotton in Australia. Second International Lygus Symposium Asilomar. J Insect Sci. 8: 49, p. 27.
- Patil BV, Bheemanna M, Patil SB, Udikery SS, Hosmani A. 2006. Record of mirid bug, Creontiades biseratense (Distant) on cotton from Karnataka, India. Insect Env. 11:176–177.
- Ravi PR, Patil BV. 2008. Biology of mirid bug, Creontiades biseratense (Distant) (Hemiptera: Miridae) on Bt cotton. Karnataka J Agric Sci. 21(2): 234–236.
- Sreedevi K, Verghese A. 2007. Ecology of aphidophagous predators in pomegranate ecosystem in India. Communication Agrl Appl Biol Sci. 72(3): 509–516.
- Surulivelu T, Dhara Jothi B. 2007. Mirid bug, Creontiodes biseratense (Distant) damage on cotton in Coimbatore. http://www.cicr.gov.in
- Trivedi TP, Yadav CP, Vishwadhar, Srivastava CP, Dhandapani, Das DK, Singh, J. 2005. Monitoring and forecasting of Heliothis / Helicoverpa population, pp. 119-140 In: Sharma HC (Ed.) Heliothis / Helicoverpa Management – Emerging trends and strategies for future research. Oxford & IBH Publishing Co. Pvt. Ltd., New Delhi.
- Udikeri SS, Patil SB, Shaila HM, Guruprasad GS, Patil SS, Kranthi KR, Khadi BM. 2009. Mirid menace – a potential emerging sucking pest problem in cotton. http://www.icas.org.
- Venkateshalu V, Kalmath B, Swamy L, Sushila N, Mallapur CP, Reddy N. 2010. Performance of different Bt cotton hybrids against mirid bug, Creontiades biseratense (Distant) (Miridae: Hemiptera). Kar J Agric Sci. 23(1):109–110.
- Venugopal Rao N. 1995. Bioecology and management of Helicoverpa armigera in the cotton ecosystem of Andhra Pradesh, Hyderabad, India. Andhra Pradesh Agricultural University. Ph.D Thesis.
- Zhao H, Ram S. 2004. Constrained cascade generalization of decision trees, IEEE Trans Knowl Data Engg 16 (6): 727–739.
- Neural-Network Classifier for the Prediction of Occurrence of Helicoverpa armigera (Hiibner) and its Natural Enemies
Authors
1 Dept. of Computer Science, Shrimathi Indira Gandhi College, Tiruchirappalli 620 002, Tamil Nadu., IN
2 Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu., IN
3 Department of Entomology, University of Agricultural Science, Raichur 584 102, Karnataka, IN
4 Indian Institute of Horticultural Research, Hessaraghatta Lake Post, Bangalore 560 089, Karnataka, IN
Source
Journal of Biological Control, Vol 25, No 2 (2011), Pagination: 134-142Abstract
The cotton bollworm, Helicoverpa armigera (Hiibner) is an important pest in India damaging cotton crop and resulting in economic loss. Accurate and timely prediction of the pest, considering biotic and abiotic factors is essential to reduce the crop loss. In this paper, we present a neural-network classifier for predicting the pest incidence on cotton by considering the season, crop phenology, biotic factors (spiders and Chrysoperla zastrowi sillemi) and abiotic factors such as maximum temperature, minimum temperature, rainfall and relative humidity. Single layer perceptron neural-network with back-propagation algorithm was utilized for the design of the presented intelligent system. Decision tree is presented from the proposed trained neural-network. The results showed that the supervised neural network system could classify or predict the pest incidence as either 'high' or 'low' based upon economic threshold level with high degree of accuracy. Extracting rules from the decision tree helps the user to understand the role of biotic and abiotic factors on H. armigera incidence.Keywords
Back-Propagation Algorithm, Biotic And Abiotic Factors, Helicoverpa armigera, Knowledge Extraction, Neuralnetwork Classifier, Pest Prediction.- Multimedia Content Protection by Biometrics-Based Scalable Encryption and Watermarking
Authors
1 Department of Computer Applications, Shrimati Indira Gandhi College, Tiruchirappalli, Tamilnadu, IN
2 Bharathidasan University & Research Guide, IN
3 Department of Computer Applications, Shrimati Indira Gandhi College, Tiruchirappalli, IN
Source
Digital Image Processing, Vol 3, No 16 (2011), Pagination: 1079-1082Abstract
With the huge development of broadband network, distribution of multimedia by means of Internet is an uncomplicated method of communication and data exchange. Intellectual Property (IP) protection is a vital component in a multimedia broadcast system. Traditional IP protection methods can be classified into two major categories: encryption and watermarking. Content protection has turned out to be one of the most considerable and demanding problems of this field. This paper proposes a multimedia content protection framework that is dependent on biometric data of the users, a layered encryption/decryption scheme and watermarking. Scalable encryption algorithms result from a transaction between implementation cost and resulting performances. In addition, this approach generally aims to be exploited competently on a large range of platforms. The computational necessities and applicability of the proposed method are addressed. By utilizing the benefit of the nature of cryptographic schemes and digital watermarking, the copyright of multimedia contents can be protected. In this paper, the scalable transmission technique is utilized over the broadcasting environment for encryption. The embedded watermark can be thus extracted with high confidence.Keywords
Multimedia, Security, Biometrics, Watermarking, Scalable Encryption.- Prediction of Secondary Structure of Using Neural Networks and Machine Learning Techniques
Authors
Source
Biometrics and Bioinformatics, Vol 4, No 1 (2012), Pagination: 46-51Abstract
One of the most significant problems in biomedical research today is the prediction of protein structure from knowledge of the primary amino acid sequence. Secondary Structure Prediction (SSP) is a very typical problem in the field of bioinformatics.Prediction of secondary structure of Proteins can be done from the Protein sequence. In the Protein structure prediction, the Amino Acid sequence of a Protein, the so-called primary structure, can be easily determined from the sequence on the Gene that codes for it. This primary structure exclusively determines a structure in its native environment. Thus primary structure plays a key role in understanding the function of the Protein. Majority of the previous research have ignored the influence of residue conformational preference on structure prediction of proteins. The primary focus of this research is to investigate a variety of approaches for employing ANN and Machine Learning techniques in order to predict the secondary structure of proteins in soybeans.
Keywords
Protein Structure Prediction, RBFNN, MELM, SVM, Amino Acid, Soybeans.- Protein Structure Prediction in Soybeans Using Neural Networks
Authors
1 Shrimati Indira Gandhi College, Trichy, IN
2 Dept. of IT & Applications, Shrimati Indira Gandhi College, Trichy, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 2 (2010), Pagination: 43-47Abstract
Proteins are a definite kind of biological macromolecules that is present in all biological organisms. Amino acids are the building blocks of proteins. They are primary structure, secondary structure, tertiary structure and quaternary structure. Most of the existing algorithms for predicting the content of the protein secondary structure elements have been based on the conventional amino acid composition, where no sequence coupling effects are taken into consideration. Prediction of three dimensional structure, secondary structure, and functional sites of proteins from primary structure are the three major problems in structural bioinformatics. More than a few different approaches have been previously used in these kind of predictions among which, artificial neural networks have been of great interest due to their capability of learning from observations and prediction of the structures for non classified instances. This paper proposes a technique for prediction of protein structure in soybeans using neural networks. This paper uses RBFNN in order to predict the secondary structure. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. The neural network architecture used in our approach is a feed forward and fully connected neural network whose Gaussian centers are optimized by genetic algorithm. Experimental are carried on dataset obtained from Protein Data Bank (PDB) to predict the structure of the protein present in it.Keywords
Amino Acids (AA), Bioinformatics, Protein Structure Prediction, Secondary Structure Content, Neural Networks, Radial Basis Function Neural Networks (RBFNN), Genetic Algorithm (GA), Protein Data Bank (PDB).- Local Texture Description Framework for Texture Based Face Recognition
Authors
1 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
3 Department of Electronics and Communication Engineering, J. P. College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 4, No 3 (2014), Pagination: 773-784Abstract
Texture descriptors have an important role in recognizing face images. However, almost all the existing local texture descriptors use nearest neighbors to encode a texture pattern around a pixel. But in face images, most of the pixels have similar characteristics with that of its nearest neighbors because the skin covers large area in a face and the skin tone at neighboring regions are same. Therefore this paper presents a general framework called Local Texture Description Framework that uses only eight pixels which are at certain distance apart either circular or elliptical from the referenced pixel. Local texture description can be done using the foundation of any existing local texture descriptors. In this paper, the performance of the proposed framework is verified with three existing local texture descriptors Local Binary Pattern (LBP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) for the five issues viz. facial expression, partial occlusion, illumination variation, pose variation and general recognition. Five benchmark databases JAFFE, Essex, Indian faces, AT & T and Georgia Tech are used for the experiments. Experimental results demonstrate that even with less number of patterns, the proposed framework could achieve higher recognition accuracy than that of their base models.Keywords
Face Recognition, Local Texture Description Framework, Nearest Neighborhood Classification, Chi-Square Distance Metric.- A Combined Approach Using Textural and Geometrical Features for Face Recognition
Authors
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
3 Department of Computer Science and Engineering, Sardar Raja College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 3, No 4 (2013), Pagination: 605-611Abstract
Texture feature plays a predominant role in recognizing face images. However different persons can have similar texture features that may degrade the system performance. Hence in this paper, the problem of face similarity is addressed by proposing a solution which combines textural and geometrical features. An algorithm is proposed to combine these two features. Five texture descriptors and few geometrical features are considered to validate the proposed system. Performance evaluations of these features are carried out independently and jointly for three different issues such as expression variation, illumination variation and partial occlusion with objects. It is observed that the combination of textural and geometrical features enhance the accuracy of face recognition. Experimental results on Japanese Female Facial Expression (JAFFE) and ESSEX databases indicate that the texture descriptor Local Binary Pattern achieves better recognition accuracy for all the issues considered.Keywords
Face Recognition, Texture Features, Geometric Features, Nearest Neighborhood Classification, Chi-Square Distance Metric.- An Illumination Invariant Texture Based Face Recognition
Authors
1 Department of Electronics and Communication Engineering, J. P. College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
3 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 4, No 2 (2013), Pagination: 709-716Abstract
Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE), Gamma Intensity Correction (GIC), Normalization chain and Modified Homomorphic Filtering (MHF) are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.Keywords
Face Recognition, Texture Analysis, Texture Features.- Fingerprint Classification Based on Recursive Neural Network with Support Vector Machine
Authors
1 A. V. V. M. Sri Pushpam College, Bharathidasan University, Tamil Nadu, IN
2 Shrimati Indira Gandhi College, Bharathidasan University, Tamil Nadu, IN